Individual differences in resting-state functional connectivity predict procrastination

2016 ◽  
Vol 95 ◽  
pp. 62-67 ◽  
Author(s):  
Yan Wu ◽  
Ling Li ◽  
Binke Yuan ◽  
Xuehong Tian
2013 ◽  
Vol 51 (13) ◽  
pp. 2918-2929 ◽  
Author(s):  
Alisha L. Janssen ◽  
Aaron Boster ◽  
Beth A. Patterson ◽  
Amir Abduljalil ◽  
Ruchika Shaurya Prakash

2021 ◽  
Author(s):  
Austin L Boroshok ◽  
Anne T Park ◽  
Panagiotis Fotiadis ◽  
Gerardo H Velasquez ◽  
Ursula A Tooley ◽  
...  

Neuroplasticity, defined as the brain's ability to change in response to its environment, has been extensively studied at the cellular and molecular levels. Work in animal models suggests that stimulation to the ventral tegmental area (VTA) enhances plasticity, and that myelination constrains plasticity. Little is known, however, about whether proxy measures of these properties in the human brain are associated with learning. Here we investigated the plasticity of the frontoparietal system (FPS), which supports complex cognition. We asked whether VTA resting-state functional connectivity and myelin map (T1-w/T2-w ratio) values predicted learning after short-term training on a FPS-dependent task: the adaptive n-back (n = 46, ages 18-25). We found that stronger connectivity between VTA and lateral prefrontal cortex at baseline predicted greater improvements in accuracy. Lower myelin map values predicted improvement in response times, but not accuracy. Our findings suggest that proxy markers of neural plasticity can predict learning in humans.


2018 ◽  
Author(s):  
Maxwell L. Elliott ◽  
Annchen R. Knodt ◽  
Megan Cooke ◽  
M. Justin Kim ◽  
Tracy R. Melzer ◽  
...  

AbstractIntrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displays higher heritability on average than resting-state functional connectivity. We also show that predictions of cognitive ability from GFC generalize across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.


2017 ◽  
Vol 118 (2) ◽  
pp. 1235-1243 ◽  
Author(s):  
Heather R. McGregor ◽  
Paul L. Gribble

We show that individual differences in preobservation brain function can predict subsequent observation-related gains in motor learning. Preobservation resting-state functional connectivity within a sensory-motor network may be used as a biomarker for the extent to which observation promotes motor learning. This kind of information may be useful if observation is to be used as a way to boost neuroplasticity and sensory-motor recovery for patients undergoing rehabilitation for diseases that impair movement such as stroke.


NeuroImage ◽  
2010 ◽  
Vol 50 (4) ◽  
pp. 1690-1701 ◽  
Author(s):  
Maarten Mennes ◽  
Clare Kelly ◽  
Xi-Nian Zuo ◽  
Adriana Di Martino ◽  
Bharat B. Biswal ◽  
...  

2021 ◽  
Author(s):  
Stephanie Matijevic ◽  
Jessica R. Andrews-Hanna ◽  
Aubrey Anne Ladd Wank ◽  
Lee Ryan ◽  
Matthew D. Grilli

The ability to generate episodic details while recollecting autobiographical events is believed to depend on a collection of brain regions that form a posterior medial network (PMN). How age-related differences in episodic detail generation relate to the PMN, however, remains unclear. The present study sought to examine individual differences, and the role of age, in PMN resting state functional connectivity (rsFC) associations with episodic detail generation. Late middle-aged and older adults (N = 41, ages 52-81), and young adults (N = 21, ages 19-35) were asked to describe recent personal events, and these memory narratives were coded for episodic, semantic and ‘miscellaneous’ details. Independent components analysis and regions-of-interest analyses were used to assess rsFC within anterior PMN connections (hippocampal and medial prefrontal) and posterior PMN connections (hippocampal, parahippocampal and parieto-occipital). Compared to younger adults, older adults produced memory narratives with lower episodic specificity (ratio of episodic:total details) and a greater amount of semantic detail. Among the older adults, episodic detail amounts and episodic specificity were reduced with increasing age. There were no significant age differences in PMN rsFC. Stronger anterior PMN rsFC was related to lower episodic detail in the older adult group, but not in the young. Among the older adults, increasing age brought on an association between increased anterior PMN rsFC and reduced episodic specificity. The present study provides evidence that functional connectivity within the PMN, particularly anterior PMN, tracks individual differences in the amount of episodic details retrieved by older adults. Furthermore, these brain-behavior relationships appear to be age-specific.


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